Embodiments of the invention generally relate to imaging and more particularly, to a technique for sinogram separation and targeted reconstruction.
Non-invasive imaging broadly encompasses techniques for generating images of the internal structures or regions of a person or object that are otherwise inaccessible for visual inspection. One of the best-known uses of non-invasive imaging is in the medical arts where these techniques are used to generate images of organs and/or bones inside a patient which would otherwise not be visible. Other well-known uses are in the field of non-destructive testing, such as for security and package screening or for quality control of manufacturing processes. Example of such non-invasive imaging modalities include X-ray based techniques, such as computed tomography (CT), as well as nuclear-based techniques, such as position emission tomography (PET) and single photon emission computed tomography (SPECT). With regard to CT imaging techniques, CT scanners typically operate by projecting fan shaped or cone shaped X-rays from an X-ray source. The X-ray source emits X-rays at numerous angles relative to an object being imaged, such as a patient, which attenuates the X-rays as they pass through. The attenuated X-rays are detected by a set of detector elements, which produce signals representing the attenuation of the incident X-rays. The signals are processed and reconstruction algorithms are employed to form images which may be evaluated themselves or which may be associated to form a volume rendering or other representation of the imaged region. In a medical context, pathologies or other structures of interest may then be located or identified from the reconstructed images or rendered volume.
CT reconstruction is usually performed using direct reconstruction techniques like the Filtered back projection (FBP) technique, based on mathematical ideals that are not typically observed in practice. One side effect of the failure of the mathematical ideals to correspond to the actual practice is that noise and resolution performance is not optimized using direct reconstruction techniques. Although these types of techniques can be performed very fast they tend to amplify the noise on the data, thereby degrading the image quality.
Iterative reconstruction techniques overcome these problems by employing various mathematical models, such as noise and system models, to account for deviations from the mathematical ideals. Iterative reconstruction techniques repeatedly apply respective forward and backward projection models to generate an image that best fits the image measurements according to an appropriate objective function. In this manner, iterative reconstruction algorithms may provide improved image quality and/or reduced X-ray dosage. In addition, iterative reconstruction algorithms may provide other benefits, such as reduction of metal artifacts in reconstructed images.
However, iterative reconstruction algorithms require significantly more computational time than conventional (direct) reconstruction methods and have thus far been impractical for mainstream CT applications. In particular, iterative reconstruction algorithms undergo many iterations to generate each image, in order to converge. Further, each iteration employs two or more computationally intensive projection and back-projection operations. As a result, iterative reconstruction algorithms may require at least an order of magnitude or more computational effort than a direct reconstruction technique to construct a single image. Consequently, iterative reconstruction approaches are typically much slower than comparable direct reconstruction approaches. It is therefore desirable to reduce the computational effort in the projection and backprojection operations.